The ‘covid & not LC’ category is notably more productive than the ‘no covid’ category in both combinings, which seems interesting.
My guess is that it's probably the combining methods, given the different results by modality. But if I had to come up with an explanation for the trend: some people tried hard to avoid COVID, which involved making their lives worse, and some people didn't. If you didn't try hard to avoid COVID, you then contracted short COVID, and got a productivity boost against the no-COVIDs by living your life normally, but no serious productivity loss from COVID.
(This continues data from the survey I did the other day on Positly (some other bits here and here.)
Before asking everyone whether they had had covid, or have ongoing problems from it, or anything else, I asked them about their recent productivity relative to 2019. I was hoping to minimize the influence of their narratives about their covid situation and how it should affect their productivity on their productivity estimates.
The clearest takeaway, I think: people who would later report ongoing post-covid symptoms also reported being way less productive on average than people who didn’t report any covid.
Perhaps stupidly, I asked people about their productivity in one of three randomly selected ways, to better tell if results were sensitive to misunderstandings, wording or other vagaries of survey taking. While I stand by this for higher commitment surveys, here it meant trying to aggregate a bunch of slightly mismatched claims about how unproductive a year people are having, when I could have just been averaging a column of numbers. And people did give different-looking distributions of answers for different questions, and different numbers of people with covid got randomized into different kinds of questions, so combining them is messy.
The different questions were these:
How productive were you this week, as a percentage of average for you in 2019?
(slider: 0-200%; instruction: For more than 200%, just put 200%)
Below are the answers, divided up by question type. Note that the numbers of respondents involved are tiny and things are noisy. For example, the category question only got two long covid sufferers, and one of them was the nurse apparently working 200% as much as in 2019 but fatigued to the point of crashing her car. (In general, productivity varying because of demand as well as supply is an obvious-in-retrospect theme, according to my reading of the long answers to later questions.)
(Probably I should have given you the ‘people who had covid more than two months ago’ numbers, but I didn’t and I’m short on time for this, sorry! All the data is available at the end.)
It would be nice to combine all these answers. A natural way to do it might be to assign plausible seeming numerical values to all the answers to 1 and 3 , then average all these values.
For instance, let’s use the following conversion:
Question 1
Question 3
With these we get:
A different thing to do is call everything below 80% ‘much less productive’ then convert everything into fraction of people avoiding that fate:
The ‘covid & not LC’ category is notably more productive than the ‘no covid’ category in both combinings, which seems interesting. Some things that might be going on:
I tend to think it’s the latter, but if anyone wants to actually do statistics, or anything else with this data:
Data